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from ultralytics import YOLO
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import random
from sklearn.linear_model import LinearRegression
st.set_page_config(page_title="Parking Traffic Predictor", layout="wide")
st.title("🚗 AI-Based Parking Space Traffic Predictor (Simulation Only)")
st.markdown("Upload a **top-view parking image**, and the system will automatically detect cars, count them, and predict parking trends — no sensors needed!")
# Load YOLOv8 model (pretrained)
@st.cache_resource
def load_model():
return YOLO("yolov8n.pt") # small model for fast inference
model = load_model()
# Upload image
uploaded_image = st.file_uploader("Upload a parking lot image", type=["jpg", "jpeg", "png"])
if uploaded_image:
st.image(uploaded_image, caption="Uploaded Image", use_container_width=True)
with st.spinner("Detecting cars..."):
results = model.predict(uploaded_image, conf=0.4)
boxes = results[0].boxes
detections = 0
# Count cars only (class 2 = car in COCO dataset)
for c in boxes.cls:
if int(c) == 2:
detections += 1
st.success(f"✅ Cars Detected: {detections}")
# Display YOLO annotated image
annotated_img = results[0].plot()
st.image(annotated_img, caption="Detected Cars", use_container_width=True)
# Simulated data for prediction
st.markdown("### 📈 Parking Traffic Prediction")
# Create fake historical data (simulating last 10 time intervals)
timestamps = pd.date_range(datetime.now().replace(minute=0, second=0), periods=10, freq='-1H')
car_counts = np.clip([detections + random.randint(-5, 5) for _ in range(10)], 0, None)
df = pd.DataFrame({"Time": timestamps, "Cars": car_counts})
df.sort_values(by="Time", inplace=True)
# Train a simple model
X = np.arange(len(df)).reshape(-1, 1)
y = df["Cars"].values
model_lr = LinearRegression().fit(X, y)
next_X = np.array([[len(df)]])
predicted_cars = int(model_lr.predict(next_X))
# Plot trend
fig, ax = plt.subplots()
ax.plot(df["Time"], df["Cars"], marker="o", label="Past Traffic")
ax.plot([df["Time"].iloc[-1] + pd.Timedelta(hours=1)], [predicted_cars],
marker="x", color="red", label="Predicted Next Hour")
ax.set_xlabel("Time")
ax.set_ylabel("Number of Cars")
ax.set_title("Parking Traffic Trend")
ax.legend()
st.pyplot(fig)
# Prediction message
if predicted_cars < detections:
st.info(f"🟢 Parking availability is **likely to increase** next hour. (Predicted {predicted_cars} cars)")
else:
st.warning(f"🔴 Parking might get **busier** next hour. (Predicted {predicted_cars} cars)")
else:
st.info("👆 Upload an image of a parking lot (top view) to get started.")
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